{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "798b3f1d-4ff0-4b8f-803c-c7a72fd75bd5",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48800820-d3b2-4b3b-b16e-006ee4b2a5cc",
   "metadata": {},
   "source": [
    "$$ y = ax + b $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "810cbdc7-8179-4bcd-8bbc-79a21de95e08",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 如果一个因变量，受到很多自变量的影响，模型y=f（x1,x2,x3,xn）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4502258-d4b5-4d07-912e-8099e44e2207",
   "metadata": {},
   "source": [
    "$$ y =w_1x_2+w_2x_2+w_3x_3+...+w_nx^n $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "f5eabfe6-c071-4d1a-8d47-58b263e5330e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_predict, train_test_split\n",
    "from sklearn import datasets\n",
    "from sklearn.datasets import fetch_california_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "d4c37c27-701b-446f-888c-56128cb57805",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
    "raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
    "data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
    "target = raw_df.values[1::2, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c5c2271f-d056-4e5d-97f2-619b66d20f3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 13)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "69d4ce48-1202-47b4-865d-2209b49401e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
       "       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
       "       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
       "       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
       "       21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
       "       35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
       "       19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
       "       20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
       "       23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
       "       33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
       "       21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
       "       20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
       "       23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
       "       15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
       "       17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
       "       25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
       "       23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
       "       32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
       "       34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
       "       20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
       "       26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
       "       31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
       "       22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
       "       42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
       "       36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
       "       32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
       "       20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
       "       20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
       "       22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
       "       21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
       "       19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
       "       32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
       "       18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
       "       16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
       "       13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
       "        7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
       "       12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
       "       27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
       "        8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
       "        9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
       "       10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
       "       15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
       "       19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
       "       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
       "       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
       "       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "cb07223d-754b-4463-aa5b-75d0fad0169d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(455, 13)\n",
      "(51, 13)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=20)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "35281f24-9ac3-450f-bbb3-fcfd4ed3e6d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([34.6, 10.8, 44. , 15. , 20.2, 20.3, 16.8, 17.8, 23.1, 23.7])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "cc1ef0d2-4770-4379-b995-08511fe3216d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-8.39875158e-02  4.65398019e-02  4.07662285e-02  2.88437643e+00\n",
      " -1.87118784e+01  3.64872383e+00  9.57491225e-03 -1.37449789e+00\n",
      "  2.89224713e-01 -1.23344189e-02 -9.42731179e-01  8.88917021e-03\n",
      " -5.40060935e-01]\n",
      "36.97170480633759\n"
     ]
    }
   ],
   "source": [
    "#模型训练\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "print(lr.coef_)\n",
    "print(lr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "29840c2f-739d-4cdf-a8ac-94e5ad9e8537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-8 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-8 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-8 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-8 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url();\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy_X',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">copy_X&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">1e-06</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('positive',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">positive&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "1188d1c3-04d4-4f81-b7f9-cc5652b05e74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 15.08073712981371\n",
      "RMSE: 3.883392476921913\n"
     ]
    }
   ],
   "source": [
    "#评估\n",
    "y_pred = lr.predict(X_test)\n",
    "from sklearn import metrics\n",
    "MSE = metrics.mean_squared_error(y_test, y_pred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
    "print('MSE:', MSE)\n",
    "print('RMSE:', RMSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffaa8fd5-2f9b-46da-a560-13d80c32265e",
   "metadata": {},
   "source": [
    "## 自行实验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "3b5f8623-8036-48c3-9d3d-57981ddec5b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_predict, train_test_split\n",
    "from sklearn import datasets\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "465dd6ce-63cd-4509-9314-516d72f94f77",
   "metadata": {},
   "outputs": [],
   "source": [
    "wm=pd.read_csv('women.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "b61a3aad-c2af-4643-9287-0e470f7086f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>59</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>61</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>62</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>63</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>65</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>67</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>68</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>69</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>70</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>71</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>72</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  height  weight\n",
       "0            1      58     115\n",
       "1            2      59     117\n",
       "2            3      60     120\n",
       "3            4      61     123\n",
       "4            5      62     126\n",
       "5            6      63     129\n",
       "6            7      64     132\n",
       "7            8      65     135\n",
       "8            9      66     139\n",
       "9           10      67     142\n",
       "10          11      68     146\n",
       "11          12      69     150\n",
       "12          13      70     154\n",
       "13          14      71     159\n",
       "14          15      72     164"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "9136f818-3804-4e0e-a0fd-2262a36c646a",
   "metadata": {},
   "outputs": [],
   "source": [
    "H = wm[['weight']]\n",
    "W = wm[['height']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb80230a-81c7-4c92-a578-2a172a53c545",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "4e174702-2eb7-4a38-b53c-a43e803376ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12, 1)\n",
      "(3, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(H, W, test_size=0.2, random_state=100)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "3cad47df-fb45-4ee2-9b8b-d66a101e0667",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.28352852]]\n",
      "[26.2150233]\n"
     ]
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "print(lr.coef_)\n",
    "print(lr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "db2eab9f-bdea-44f2-8355-bd821a55938c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.1465724638615403\n",
      "RMSE: 0.3828478338211414\n"
     ]
    }
   ],
   "source": [
    "y_pred = lr.predict(X_test)\n",
    "from sklearn import metrics\n",
    "MSE = metrics.mean_squared_error(y_test, y_pred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
    "print('MSE:', MSE)\n",
    "print('RMSE:', RMSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6e9fdb1-eb3e-49c1-afb5-35016d81e465",
   "metadata": {},
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "26574e69-b4ea-4142-9d48-af43e355bd6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "# 导入sklearn自带的iris数据集\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "f09097d3-10eb-476d-8c9b-0a8424e337c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(iris.data,iris.target,test_size=0.3,random_state=123456)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "f8f0e231-ad9c-45d3-b05e-7ed4d91ab231",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-9 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-9 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-9 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-9 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-9 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-9 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-9 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-9 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url();\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_neighbors',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('weights',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">weights&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('algorithm',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">algorithm&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('leaf_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
       "            <td class=\"value\">30</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('p',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">p&nbsp;</td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('metric',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">metric&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('metric_params',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">metric_params&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# 实例化模型并训练\n",
    "model = KNeighborsClassifier()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "3eb14602-8929-4285-905e-c0fa6bc2665a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9777777777777777\n"
     ]
    }
   ],
   "source": [
    "print(model.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "90ccf6e0-8f0c-4973-863e-f28a0f781013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        15\n",
      "           1       1.00      0.93      0.97        15\n",
      "           2       0.94      1.00      0.97        15\n",
      "\n",
      "    accuracy                           0.98        45\n",
      "   macro avg       0.98      0.98      0.98        45\n",
      "weighted avg       0.98      0.98      0.98        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test) #预测类别标签\n",
    "y_pred_prob = model.predict_proba(X_test) #预测类别概率\n",
    "\n",
    "# 分类评估报告classification_report\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8f937b0-5c01-4854-b8b9-879611eb9ebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "precision 表示模型在预测某一特定类别时的准确性。它关注的是所有预测为正样本的样本中有多少是真正的正样本,\n",
    "也就是判断正样品的能力\n",
    "recall找出正样本的能力\n",
    "f1-score \n",
    "support\n",
    "accuracy 模型预测正确的样本数占总样本数的比例，预测正确的能力"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8071e084-06e7-44b1-901f-0aff92304cad",
   "metadata": {},
   "source": [
    "## 作业"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e72108e3-1d29-41bd-a64a-a3064eb61320",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_predict, train_test_split\n",
    "from sklearn import datasets\n",
    "from sklearn.datasets import fetch_california_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d3265a3c-17b1-4aa8-b8c7-9ac5fefa305c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_excel('Folds5x2_pp.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cb2d4398-c53b-40e0-ae14-8ae93bb8bacd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AT</th>\n",
       "      <th>V</th>\n",
       "      <th>AP</th>\n",
       "      <th>RH</th>\n",
       "      <th>PE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.96</td>\n",
       "      <td>41.76</td>\n",
       "      <td>1024.07</td>\n",
       "      <td>73.17</td>\n",
       "      <td>463.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.18</td>\n",
       "      <td>62.96</td>\n",
       "      <td>1020.04</td>\n",
       "      <td>59.08</td>\n",
       "      <td>444.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.11</td>\n",
       "      <td>39.40</td>\n",
       "      <td>1012.16</td>\n",
       "      <td>92.14</td>\n",
       "      <td>488.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.86</td>\n",
       "      <td>57.32</td>\n",
       "      <td>1010.24</td>\n",
       "      <td>76.64</td>\n",
       "      <td>446.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.82</td>\n",
       "      <td>37.50</td>\n",
       "      <td>1009.23</td>\n",
       "      <td>96.62</td>\n",
       "      <td>473.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9563</th>\n",
       "      <td>16.65</td>\n",
       "      <td>49.69</td>\n",
       "      <td>1014.01</td>\n",
       "      <td>91.00</td>\n",
       "      <td>460.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9564</th>\n",
       "      <td>13.19</td>\n",
       "      <td>39.18</td>\n",
       "      <td>1023.67</td>\n",
       "      <td>66.78</td>\n",
       "      <td>469.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9565</th>\n",
       "      <td>31.32</td>\n",
       "      <td>74.33</td>\n",
       "      <td>1012.92</td>\n",
       "      <td>36.48</td>\n",
       "      <td>429.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9566</th>\n",
       "      <td>24.48</td>\n",
       "      <td>69.45</td>\n",
       "      <td>1013.86</td>\n",
       "      <td>62.39</td>\n",
       "      <td>435.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9567</th>\n",
       "      <td>21.60</td>\n",
       "      <td>62.52</td>\n",
       "      <td>1017.23</td>\n",
       "      <td>67.87</td>\n",
       "      <td>453.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9568 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         AT      V       AP     RH      PE\n",
       "0     14.96  41.76  1024.07  73.17  463.26\n",
       "1     25.18  62.96  1020.04  59.08  444.37\n",
       "2      5.11  39.40  1012.16  92.14  488.56\n",
       "3     20.86  57.32  1010.24  76.64  446.48\n",
       "4     10.82  37.50  1009.23  96.62  473.90\n",
       "...     ...    ...      ...    ...     ...\n",
       "9563  16.65  49.69  1014.01  91.00  460.03\n",
       "9564  13.19  39.18  1023.67  66.78  469.62\n",
       "9565  31.32  74.33  1012.92  36.48  429.57\n",
       "9566  24.48  69.45  1013.86  62.39  435.74\n",
       "9567  21.60  62.52  1017.23  67.87  453.28\n",
       "\n",
       "[9568 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6d857733-b98c-44ce-a316-0dab2ac05ef4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9568 entries, 0 to 9567\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   AT      9568 non-null   float64\n",
      " 1   V       9568 non-null   float64\n",
      " 2   AP      9568 non-null   float64\n",
      " 3   RH      9568 non-null   float64\n",
      " 4   PE      9568 non-null   float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 373.9 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3882d9b8-c6f6-4d33-b9bb-0893cc72b342",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7654, 4)\n",
      "(1914, 4)\n"
     ]
    }
   ],
   "source": [
    "X=data[['AT','V','AP','RH']]\n",
    "y=data['PE']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=100)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0569e98c-20c3-483a-b8c8-e40b67d9f73e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.98576321 -0.22958003  0.05652403 -0.15867199]\n",
      "460.2499256535003\n",
      "MSE: 23.298947579513733\n",
      "RMSE: 4.826898339463318\n"
     ]
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "print(lr.coef_)\n",
    "print(lr.intercept_)\n",
    "\n",
    "y_pred = lr.predict(X_test)\n",
    "from sklearn import metrics\n",
    "MSE = metrics.mean_squared_error(y_test, y_pred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
    "print('MSE:', MSE)\n",
    "print('RMSE:', RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d52b27e7-7522-4100-bd31-6006817c62a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.family'] = ['sans-serif']\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "mpl.rcParams['axes.unicode_minus']=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e3d81026-c376-4a75-ac2b-f18930e6b78f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(range(len(y_test)), y_test, 'r', label='测试数据')\n",
    "plt.plot(range(len(y_test)), y_pred, 'b', label='预测数据')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bae6b918-c608-4314-90b4-d461da983bb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred,color=\"green\",s=3)\n",
    "plt.plot([y_test.min(),y_test.max()], [y_test.min(),y_test.max()], linestyle=\"--\",color='black')\n",
    "plt.xlabel('真实值')\n",
    "plt.ylabel('预测值')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "46f1e812-4ba9-4ec8-95d4-d2fc1bff078a",
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75e5ccb9-433b-4b77-b85d-a05a5a5c94cc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
